A technique for improving the max-min ant system algorithm
In recent years, various metaheuristic approaches have been created to solve Quadratic Assignment Problems (QAPs). Among others is the Ant Colony Optimization (ACO) algorithm, which was inspired by the foraging behavior of ants. Although it has solved some QAPs successfully, it still contains some w...
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| Format: | Book Section |
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Institute of Electrical and Electronics Engineers
2008
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| _version_ | 1796855085351829504 |
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| author | Phen, Chiak See Kuan, Yew Wong Komarudin, Komarudin |
| author_facet | Phen, Chiak See Kuan, Yew Wong Komarudin, Komarudin |
| author_sort | Phen, Chiak See |
| collection | ePrints |
| description | In recent years, various metaheuristic approaches have been created to solve Quadratic Assignment Problems (QAPs). Among others is the Ant Colony Optimization (ACO) algorithm, which was inspired by the foraging behavior of ants. Although it has solved some QAPs successfully, it still contains some weaknesses and is unable to solve large QAP instances effectively. Thereafter, various suggestions have been made to improve the performance of the ACO algorithm. One of them is through the development of the Max-Min Ant System (MMAS) algorithm. In this paper, a discussion will be given on the working structure of MMAS and its associated weaknesses or limitations. A new strategy that could further improve the search performance of MMAS will then be presented. Finally, the results of an experimental evaluation conducted to evaluate the usefulness of this new strategy will be described. |
| first_indexed | 2024-03-05T18:23:27Z |
| format | Book Section |
| id | utm.eprints-12481 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:23:27Z |
| publishDate | 2008 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | utm.eprints-124812017-10-02T07:36:22Z http://eprints.utm.my/12481/ A technique for improving the max-min ant system algorithm Phen, Chiak See Kuan, Yew Wong Komarudin, Komarudin TJ Mechanical engineering and machinery TS Manufactures In recent years, various metaheuristic approaches have been created to solve Quadratic Assignment Problems (QAPs). Among others is the Ant Colony Optimization (ACO) algorithm, which was inspired by the foraging behavior of ants. Although it has solved some QAPs successfully, it still contains some weaknesses and is unable to solve large QAP instances effectively. Thereafter, various suggestions have been made to improve the performance of the ACO algorithm. One of them is through the development of the Max-Min Ant System (MMAS) algorithm. In this paper, a discussion will be given on the working structure of MMAS and its associated weaknesses or limitations. A new strategy that could further improve the search performance of MMAS will then be presented. Finally, the results of an experimental evaluation conducted to evaluate the usefulness of this new strategy will be described. Institute of Electrical and Electronics Engineers 2008 Book Section PeerReviewed Phen, Chiak See and Kuan, Yew Wong and Komarudin, Komarudin (2008) A technique for improving the max-min ant system algorithm. In: Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development. Institute of Electrical and Electronics Engineers, New York, 863-866 . ISBN 978-142441692-9 http://dx.doi.org/10.1109/ICCCE.2008.4580728 DOI:10.1109/ICCCE.2008.4580728 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Phen, Chiak See Kuan, Yew Wong Komarudin, Komarudin A technique for improving the max-min ant system algorithm |
| title | A technique for improving the max-min ant system algorithm |
| title_full | A technique for improving the max-min ant system algorithm |
| title_fullStr | A technique for improving the max-min ant system algorithm |
| title_full_unstemmed | A technique for improving the max-min ant system algorithm |
| title_short | A technique for improving the max-min ant system algorithm |
| title_sort | technique for improving the max min ant system algorithm |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
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